Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance

Timely and efficacious antibiotic treatment depends on precise and quick in silico antimicrobial-resistance predictions. Limited treatment choices due to antimicrobial resistance (AMR) highlight the necessity to optimize the available diagnostics. AMR can be explicitly anticipated on the basis of ge...

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Bibliographic Details
Published in:	Antibiotics (Basel) Vol. 11; no. 11; p. 1593
Main Authors:	Yasir, Muhammad, Karim, Asad Mustafa, Malik, Sumera Kausar, Bajaffer, Amal A., Azhar, Esam I.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2022 MDPI
Subjects:	Accuracy Algorithms Antibiotics Antimicrobial agents Antimicrobial resistance Bacterial infections Ceftazidime Ciprofloxacin Comparative analysis Data mining Decision trees Drug resistance Drug resistance in microorganisms Gene expression Genomes Genomics Infections Learning algorithms Machine learning Meropenem Multidrug resistance Multidrug resistant organisms Nucleotide sequence Pathogens Patients Performance evaluation Physicians Prediction models Pseudomonas aeruginosa Transcriptomes Transcriptomics Saudi Arabia Taiwan
ISSN:	2079-6382, 2079-6382
Online Access:	Get full text
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